Cardiovascular diseases are the leading cause of death globally, and the leading cause of hospital admissions in the U.S. and Europe. More than 26 million people worldwide suffer from heart failure each year, and about half of these patients die within five years. Clinical heart failure is a progressive syndrome where impaired ventricular function results in inadequate systemic perfusion. The diagnosis of heart failure conventionally relies on clinical history, physical examination, basic lab tests, and imaging. This diagnostic rubric has not changed in three decades, and lacks the ability to accurately sub-classify patients into the numerous potential clinical etiologies, which in turn has limited the development of new treatments. However, when the cause of heart failure is unidentified, endomyocardial biopsy (EMB) represents the gold standard for evaluation and grading of heart disease.
Conventional approaches to analyzing EMBs are not optimal. Manual interpretation of EMB suffers from high inter-rater variability in the pathologic diagnosis of heart disease. Manual interpretation of EMBs by expert human pathologists has an accuracy of only approximately 75% when classifying an EMB as indicating heart failure or non-heart failure. Furthermore, manual interpretation of EMB has limited clinical indications. Meanwhile, the increasingly common digitization of glass pathology slides has lead to a proliferation of whole-slide imaging (WSI) platforms.
Conventional image analysis approaches that employ digitized WSI images may involve manually engineering features. The manually engineered features may include intensity statistics, texture descriptors, or image decompositions. These features are then provided to a supervised machine learning algorithm for classification or regression. Designing discriminative features is a long process that requires computational experience and domain knowledge to develop features that might, potentially, be relevant to the intended classification. Furthermore, designing discriminative features may leave out relevant or even currently biologically unknown features.